The signal of diffusion-weighted imaging of the human kidney differs from the signal in brain examinations due to the different microscopic structure of the tissue. In the kidney, the deviation of the signal behavior of monoexponential characteristics is pronounced. The aim of the study was to analyze whether a mono-or biexponential or a distribution function model fits best to describe diffusion characteristics in the kidney. To determine the best regression, different statistical parameters were utilized: correlation coefficient (R 2 ), Akaike's information criterion, Schwarz criterion, and F-test (F ratio ). Additionally, simulations were performed to analyze the relation between the different models and their dependency on signal noise. Statistical tests showed that the biexponential model describes the signal of diffusion-weighted imaging in the kidney better than the distribution function model. The monoexponential model fits the diffusionweighted imaging data the least but is the most robust against signal noise. From a statistical point of view, diffusion-weighted imaging of the kidney should be modeled biexponentially under the precondition of sufficient signal to noise. Magn Reson Med 64:616-622,

DTI-derived FA values are a sensitive measure to discriminate healthy from functionally impaired human sciatic nerve segments. DTI of proximal nerve segments may be useful for estimating the proximal axonal degeneration burden in patients with peripheral neuropathies.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson’s disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action-control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young–old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way.

Purpose Parkinson’s disease (PD) is primarily defined by motor symptoms and is associated with alterations of sensorimotor areas. Evidence for network changes of the sensorimotor network (SMN) in PD is inconsistent and a systematic evaluation of SMN in PD yet missing. We investigate functional connectivity changes of the SMN in PD, both, within the network, and to other large-scale connectivity networks. Methods Resting-state fMRI was assessed in 38 PD patients under long-term dopaminergic treatment and 43 matched healthy controls (HC). Independent component analysis (ICA) into 20 components was conducted and the SMN was identified within the resulting networks. Functional connectivity within the SMN was analyzed using a dual regression approach. Connectivity between the SMN and the other networks from group ICA was investigated with FSLNets. We investigated for functional connectivity changes between patients and controls as well as between medication states (OFF vs. ON) in PD and for correlations with clinical parameters. Results There was decreased functional connectivity within the SMN in left inferior parietal and primary somatosensory cortex in PD OFF. Across networks, connectivity between SMN and two motor networks as well as two visual networks was diminished in PD OFF. All connectivity decreases partially normalized in PD ON. Conclusion PD is accompanied by functional connectivity losses of the SMN, both, within the network and in interaction to other networks. The connectivity changes in short- and long-range connections are probably related to impaired sensory integration for motor function in PD. SMN decoupling can be partially compensated by dopaminergic therapy.